System and method for canceling interference in a communication system

ABSTRACT

A filter settings generation operation includes sampling a communication channel to produce a sampled signal. The sampled signal is spectrally characterized across a frequency band of interest to produce a spectral characterization of the sampled signal. This spectral characterization may not include a signal of interest. The spectral characterization is then modified to produce a modified spectral characterization. Filter settings are then generated based upon the modified spectral characterization. Finally, the communication channel is filtered using the filter settings when the signal of interest is present on the communication channel. In modifying the spectral characterization, pluralities of spectral characteristics of the spectral characterization are independently modified to produce the modified spectral characterization. Modifications to the spectral characterization may be performed in the frequency domain and/or the time domain. One particular spectral modification that is performed is raising of the noise floor of the spectral characterization to meet a budgeted signal-to-noise ratio. Other spectral modifications include modifying spectral components corresponding to an expected interfering signal. In modifying these spectral characterizations, spectral components corresponding to a plurality of expected interfering signals may be modified.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to U.S. Provisional Application Ser.No. 60/262,380, filed Jan. 16, 2001, the disclosure of which isincorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates generally to communication systems, andmore particularly to interference mitigation in communication systems.

BACKGROUND OF THE INVENTION

The structure and operation of high-speed data communication systems aregenerally known. Such high-speed data communication systems employvarious media and/or wireless links to support the transmission ofhigh-speed data communications. Particular embodiments of high-speedcommunication systems include, for example, cable modem systems, homenetworking systems, wired local area networks, wired wide area networks,wireless local area networks, satellite networks, etc. Each of thesehigh-speed data communication systems has some unique operationalcharacteristics. Further, some of these high-speed data communicationsystems share similar operational drawbacks. Home networking systems andcable modem systems, for example, are both subject to interferingsignals that are coupled on media that carry communication signals.

Cable modem systems, and more generally, cable telecommunication systemsinclude set-top boxes and residential gateways that are in combinationcapable of currently providing data rates as high as 56 Mbps, and arethus suitable for high-speed file transfer, video teleconferencing andpay-per-view television. These cable telecommunication systems maysimultaneously provide high-speed Internet access, digital television(such as pay-per-view) and digital telephony. Such a system is shown anddescribed in U.S. patent application Ser. No. 09/710,238 (AttorneyDocket No. 34690), entitled “Pre-Equalization Technique for UpstreamCommunication Between Cable Modem and Headend”, filed on Nov. 9, 2000,the disclosure of which is expressly incorporated by reference.

Cable modems are used in a shared access environment in whichsubscribers compete for bandwidth that is supported by shared coaxialcables. During normal operations, sufficient bandwidth is availableacross the shared coaxial cables to service a large number ofsubscribers, with each subscriber being serviced at a high data rate.Thus, during normal operations, each subscriber is provided a data ratethat is sufficient to service uninterrupted video teleconferencing,pay-per-view television, and other high bandwidth services.

Intermittent, narrowband interfering signals may, from time to time,interfere with wideband communication signals, e.g., upstreamData-Over-Cable Service Interface Specification (DOCSIS) transmissions(“desired signals”). These intermittent narrowband interfering signalsunintentionally couple to the shared coaxial cables via deficiencies inshielding and/or other coupling paths. With these interfering signalspresent, the data rate that is supportable on the coaxial cables isreduced. In some cases, depending upon the strength and band of theinterfering signals, the supportable bandwidth is reduced by asignificant level.

Conventionally, when an interfering signal is present, an adaptivecancellation filter is employed by each cable modem receiver to cancelthe interfering signal by adaptively placing a filtering notch or nullat the frequency of the interfering signal. When the interfering signalbecomes absent, the conventional adaptive cancellation filter continuesto adapt and removes the filtering notch. If the interfering signalreappears, the adaptive cancellation filter again adapts to null theinterfering signal. Thus, when the interfering signal first reappears,the cancellation filter cannot fully compensate for the interferingsignal. Because many interfering signals are intermittent, the presenceof these intermittent signals reduces the bandwidth that is supportableupon the coaxial media during the time period required for thecancellation filter to adapt. Further, because the interfering signalsoftentimes vary in strength while present, the adaptive cancellationfilter most often times does not fully remove the interfering signal.

Overlapping adjacent channel signals cause another source ofinterference for the desired signal because they often produceinterfering signals in the band of the desired signal. For example, aTDMA signal that resides in an adjacent channel and that turns on andoff may have side lobes that overlap and interfere with the desiredsignal. When the interfering signal is present, the conventionaladaptive cancellation filter places a notch or null at the frequencyband of the interfering signal. When the interfering signal is absent,the cancellation filter adapts to removes the notch. The precise amountof interference in the desired signal caused by the adjacent channelsignals may vary with data content in the adjacent channel.

Thus, in both the case of the narrowband interferer and the adjacentchannel interferer, the interfering signal(s) varies over time. For thisreason, an optimal or near-optimal solution may be found only for theaverage interfering strength of the interfering signal(s), but not forthe peak(s) of the interfering signal(s). In many operationalconditions, typical fluctuations in the strength of interfering signalscause conventional cancellation filters to provide insufficientcancellation. Resultantly, overall bandwidth that could be provided bythe supporting communication system on the particular shared media issignificantly reduced.

Therefore, there is a need in the art for a filtering system andassociated operations that cancel interfering signals so that throughputis maximized.

SUMMARY OF THE INVENTION

The present invention specifically addresses and alleviates theabove-mentioned deficiencies associated with the prior art toefficiently filter a communication channel to remove narrow bandinterfering signals. According to the present invention, a communicationchannel is sampled to produce a sampled signal. Then, the sampled signalis spectrally characterized across a frequency band of interest toproduce a spectral characterization of the sampled signal. This spectralcharacterization may not include the signal of interest. The spectralcharacterization is then modified to produce a modified spectralcharacterization. Filter settings are then generated based upon themodified spectral characterization. Finally, the communication channelis filtered using the filter settings when the signal of interest ispresent on the communication channel.

Various operations are employed to modify the spectral characterizationto produce the modified spectral characterization. In most cases, aplurality of spectral characteristics of the spectral characterizationare independently modified to produce the modified spectralcharacterization. Modifications to the spectral characterization may beperformed in the frequency domain and/or the time domain. One particularspectral modification that is performed is raising of the noise floor ofthe spectral characterization to meet a budgeted signal-to-noise ratio.

Other spectral modifications include modifying spectral componentscorresponding to an expected interfering signal. In modifying thesespectral characterizations, spectral components corresponding to aplurality of expected interfering signals may be modified. In obtaininginformation to perform this spectral modification, spectral componentsof prior sampled signals may be employed. For example, an interferingsignal may be intermittent so that it is not present in the currentsampled signal but was present in prior sampled signals. Since overallsystem performance may be enhanced when the filter compensates for thisinterfering signal even when it is not present, in modifying thespectral characterization of the sampled signal to produce the modifiedspectral characterization, the presence of an interfering signal in aprior sampled signal may be weighted more heavily than the absence ofthe interfering signal in the current sampled signal.

The communication channel may be sampled with or without the presence ofthe signal of interest. However, when the signal of interest is presenton the communication channel during the sampling interval, the signal ofinterest must be removed from the sampled signal. Further, when thetotal spectral density of the sampled signal exceeds a threshold value,the sampled signal may be discarded as invalid.

Because the filtering operations of the present invention adapt to timevarying interfering signals in an adaptive fashion, more efficientfiltering operations are performed. For example, by raising the noisefloor of the spectral characterization, budgeted SNR design constraintsmay be met with minimal filter tap magnitudes. Further, by enabling theintroduction of known interferers into the modified spectralcharacterization, the filtering operations will compensate forinterfering signals immediately when they appear, not after a learningperiod of the prior filtering operations. Because of these benefits,among others, greater system throughput is realized.

Other features and advantages of the present invention will becomeapparent from the following detailed description of the invention madewith reference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects and advantages of the presentinvention will be more fully understood when considered with respect tothe following detailed description, appended claims and accompanyingdrawings wherein:

FIG. 1 is a simplified block diagram illustrating a first embodiment ofa narrow band interference cancellation filter (ICF) used in accordancewith the principles of the present invention;

FIG. 2 is a simplified block diagram illustrating a set of operationsemployed to generate the filter settings of FIG. 1;

FIG. 3 is a block diagram illustrating operations employed to generatefilter settings corresponding to a first embodiment of FIG. 2;

FIG. 4 is a block diagram illustrating operations employed to generatefilter settings corresponding to a second embodiment of FIG. 2;

FIG. 5 is a graph illustrating the spectral characteristics of a sampledsignal according to the present invention;

FIG. 6 is a graph illustrating the modified spectral characteristics ofa sampled signal according to the present invention;

FIG. 7A is a simplified block diagram illustrating an embodiment of anarrow band interference cancellation filter used in accordance with theprinciples of the present invention;

FIG. 7B is a block diagram of a filter weight computation algorithm forthe ICF of FIG. 7A;

FIG. 8 is a logic diagram illustrating operations performedcorresponding to the filter weight computation algorithm of FIG. 7B;

FIG. 9 is a table of modes used in connection with a spectral maskincorporated in the algorithm of FIG. 7B;

FIG. 10 is a diagram illustrating a non-linear function used inconnection with the tracker of the filter weight computation algorithmof FIG. 7B;

FIG. 11 is a flow diagram depicting a noise floor setting algorithm forthe filter weight computation algorithm of FIG. 7B;

FIG. 12 is a flow diagram depicting a modified noise floor settingalgorithm for the filter weight computation algorithm of FIG. 7B; and

FIG. 13 is a system diagram illustrating the manner in which the presentinvention may be employed in a cable telecommunication system servicinghigh speed data communications.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a simplified block diagram illustrating a first embodiment ofa narrow band interference cancellation filter (ICF) used in accordancewith the principles of the present invention. According to the presentinvention, incoming unfiltered communications 102 are received on acommunication channel and filtered using filter 104 to produce filteredcommunications 110. Filter settings 108, generated by filter settingsgeneration block 106, when applied to filter 104 remove narrow bandinterfering signals from the communication channel to produce thefiltered communications 110. Thus, during normal operations, thefiltered communications 110 are provided to the receiver section of adevice in which the ICF resides. Because of the filtering operations ofthe present invention, the receiver is better able to operate on asignal of interest (desired signal) contained in the filteredcommunications 110 to remove data contained therein.

FIG. 2 is a simplified block diagram illustrating a set of operationsemployed to generate the filter settings 108 of FIG. 1. In operation,the filter settings generation block 106 first samples the communicationchannel 200 to produce a sampled signal 204. Sampling operations areperformed by the signal sampler 202 of FIG. 2. In the particularembodiment of operation illustrated, the communication channel 200 issampled when a signal of interest (desired signal) is not present on thecommunication channel 200. However, in other operational embodiments,the signal of interest is present on the communication channel 200during sampling but all or a portion of the signal of interest isremoved from the sampled signal 204 by the signal sampler 202, takingadvantage of a known preamble, for example.

The sampled signal 204 is then spectrally characterized across afrequency band of interest by spectral characterization block 206 toproduce a spectral characterization 208 of the sampled signal 204.Because the sampled signal 204 did not include the signal of interest,the spectral characterization 208 also does not include the signal ofinterest 212. Thus, the sampled signal 204 includes noise that ispresent during the sampling period and includes interfering signal(s) ifpresent. The filter settings generation block 106 operates periodicallyto generate new filter settings 108. Thus, during some sample periods,interfering signals are present while during other sample periods,interfering signals are not present. Therefore, for any given operationof the filter settings generation block, the spectral characteristics208 may or may not include interfering signals. However, the spectralcharacteristics 208 will always include noise at some level.

Next, the spectral characteristics 208 of the sampled signal 204 aremodified to produce a modified spectral characterization 212 by modifyspectral characterization block 210. The modifications performed bymodify spectral characterization block 210 to produce the modifiedspectral characterization 212 are numerous and varied. These operationsinclude raising the noise floor of the spectral characterization 208,introducing spectral characteristics into the spectral characterization208 that relate to previously present interfering signals, introducingspectral characteristics of expected interfering signals into thespectral characterization 208 that are expected but that have notrecently been present (or at all been present), and other preemptivemodifications.

A filter settings generation block 214 then generates filter settings108 based upon the modified spectral characterization 212. Finally, thefilter settings 108 are applied to the filter 104 for subsequentfiltering of the communication channel when the signal of interest ispresent on the communication channel.

FIG. 3 is a block diagram illustrating operations employed to generatefilter settings corresponding to a first embodiment of FIG. 2. As shownin FIG. 2, the communication channel is sampled using signal sampler 302to produce a sampled signal 304 in the time domain. The sampled signal304 is produced by the signal sampler 302 so that a signal of interestis not present (or substantially attenuated). The sampled signal 304 isa digital representation of the analog signal that is present on thecommunication channel 300 during the sampling interval.

A Fast Fourier Transform (FFT) is then performed upon the sampled signal304 by spectral characterization block 306. A Hanning or other window isused to remove sensitivity to frequency location. The FFT operation (aswell as most/all other of the operations upon the digital data) isperformed using a Digital Signal Processor (DSP) or another computingdevice resident in the device performing the operations of the presentinvention. The spectral characterization block 306 performing the FFToperation therefore produces a spectral characterization 308 of thesampled signal. Because no signal of interest (desired signal) waspresent in the sampled signal 304, no spectral components of the signalof interest (desired signal) are present in the spectralcharacterization 308.

As is illustrated, the spectral characterization block 306 of FIG. 3receives input in the time domain but produces output in the frequencydomain. The concepts of transformation from the time domain to thefrequency domain and the transformation from the frequency domain to thetime domain are well known and will be described herein only as theyrelate to the present invention. Modification of the spectral componentsof the sampled signal may be performed in the time domain and/or thefrequency domain. In the embodiment of FIG. 3, modification of spectralcomponents is first performed in the frequency domain by modify spectralcharacterization block 310 to produce a modified spectralcharacterization 312. Such modification of the spectral components bythe modify spectral characterization block 310 may include raising thenoise floor of the spectral characterization 308, introducing spectralcharacteristics that relate to previously present interfering signals,and introducing spectral characteristics of expected interferingsignals, among others.

The modified spectral characterization 312 is then converted back to thetime domain as modified spectral characterization 316 via Inverse FastFourier Transform (IFFT). In the time domain, the modified spectralcharacterization 316 is contained in an autocorrelation matrix, which isapplied to a filter settings generation block 318 that produces thefilter settings 320. Note that during the generation of the filtersettings, the modified spectral characterization 316 of the sampledsignal may be further modified. In the time domain, the modifiedspectral characterization 316 is further modified by modifying thecomponents of the corresponding autocorrelation matrix. These furthermodifications may be required to generate filter settings that meet aset of filter settings criteria. Such further modification (as will bedescribed further with reference to FIGS. 7B and 8) may include furtherraising the noise floor of the spectrum corresponding to the modifiedautocorrelation characterization 316 and using prior filter settings inorder to generate a satisfactory set of filter settings.

FIG. 4 is a block diagram illustrating operations employed to generatefilter settings corresponding to a second embodiment of FIG. 2. Ascontrasted to the operations of FIG. 3, the operations of FIG. 4 areperformed fully in the time domain. As shown in FIG. 3, thecommunication channel is sampled using signal sampler 412 to produce asampled signal 414 in the time domain that either does not contain thesignal of interest or from which the signal of interest wassubstantially attenuated. This sampled signal 414 is then presented toautocorrelation block 416 that determines the autocorrelation functionof the sampled signal 414. The result of this operation is thegeneration of a spectral characterization 418 in the time domain of thesampled signal 414 in the form of an autocorrelation matrix.

The modify autocorrelation characterization block 420 operates on thespectral characterization 418 of the sampled signal 414 in the timedomain by altering the coefficients of the autocorrelation matrix. Thealtered autocorrelation matrix therefore represents the modifiedspectral characterization 422 that is in the time domain. Modificationof the spectral components of the spectral characterization 418 by themodify autocorrelation characterization block 420 may include raisingthe noise floor of the spectral characterization 418, introducingspectral characteristics that relate to previously present interferingsignals, and introducing spectral characteristics of expectedinterfering signals that have not yet been present, among others. Thenoise floor of the modified spectral characterization 418 of the sampledsignal 414 may be changed by altering the diagonal components of theautocorrelation matrix. Other spectral characteristics of the spectralcharacterization 418 of the sampled signal 414 may be changed byaltering the off-diagonal components of the autocorrelation matrix.

The modified spectral characterization 422 is applied to a filtersettings generation block 424 that produces the filter settings 426.Note that during the generation of the filter settings, the modifiedspectral characterization 422 of the sampled signal may be furthermodified. Such further modification may be required to generate filtersettings that meet a set of filter settings criteria. Such furthermodification (as will be described further with reference to FIGS. 7Band 8) may include further raising the noise floor of the modifiedspectral characterization 422 and using prior filter settings in orderto generate a satisfactory set of filter settings. Spectralcharacteristics of the modified spectral characterization 422 arefurther altered in the filter settings generation block 424 by modifyingthe components of the corresponding autocorrelation matrix.

FIG. 5 is a graph illustrating the spectral characteristics of a sampledsignal according to the present invention. The representation of thesampled signal of FIG. 5 is in the frequency domain, e.g., spectralcharacterization 308 of FIG. 3. In one embodiment of the presentinvention, the spectral characterization will include 256 unique“frequency bins” within a frequency band of interest. Each frequency bincorresponds to a unique frequency, includes a magnitude component andmay include an angular component. In a particular implementation of thepresent invention, the represented magnitude represents the power of thesampled signal, e.g., signal magnitude squared, without an angularcomponent. Further, in the particular embodiment, the representedmagnitude is expressed in a logarithmic scale, e.g., log base 2, foreach frequency bin.

Thus, in the graph of FIG. 5, the frequency bins include components for1, 2, . . . , N, where N=256. The spectral characterization of thesampled signal includes a spectral component for each frequency bin,denoted as S_(f1), S_(f2), . . . , S_(fN). Because no signal of interest(desired signal) is present in the spectral characterization of FIG. 5,each spectral component corresponds to either noise or a combination ofnoise and a sampled narrowband interferer. For example, spectralcomponents S_(f3) and S_(f4) correspond to a particular narrowbandinterferer that was present on the communication channel during thesampling interval. Other spectral components corresponding tointerfering signals that are present during the sampling interval mayalso be present but are not shown in the example of FIG. 5.

Also illustrated in FIG. 5 is a representation of a noise floor for thespectral characterization. The level of the noise floor may becalculated as an average of spectral components that do not exceed athreshold, an average of all spectral components, a weighted average ofall or a portion of the spectral components, or in another manner thataccurately represents the noise floor for the sampled signal.

Also illustrated in FIG. 5 is the concept of a budgeted signal to noiseratio (SNR) for the communication channel. As is generally known, in thedesign of a receiver, a minimum SNR is desired for satisfactorydemodulation operations to be performed. This minimum SNR is referred toherein as the budgeted SNR. Based upon this budgeted SNR and an estimateof the signal strength of the signal of interest (desired signal) thatwill be received by the receiver, an estimated level of an acceptablenoise floor is determined. As is illustrated in FIG. 5, the samplednoise floor lies below the difference between the magnitude of thesignal of interest (desired signal) and the budgeted SNR. Thus, in theexample of FIG. 5, the receiver could operate within its design criteriaeven if the noise floor of the communication was higher than the samplednoise floor.

FIG. 6 is a graph illustrating the modified spectral characteristics ofa sampled signal according to the present invention. As compared to thespectral characterization of FIG. 5, the modified spectralcharacterization includes a raised noise floor and an added narrowbandinterferer at frequency bin f7. In raising the noise floor, themagnitude of some, most, or all of the spectral components is raiseduntil the raised noise floor corresponds to the difference between theexpected magnitude of the signal of interest (desired signal) and thebudgeted SNR. In adding a spectral component of the added narrowbandinterferer, the magnitude of one or more spectral componentscorresponding to the added narrowband interferer is increased to adesired magnitude. The modified spectral components are then used togenerate the filter settings.

The spectral component modifications illustrated with reference to FIGS.5 and 6 are performed in the frequency domain. However, similaroperations may also be performed in the time domain by operating uponthe autocorrelation matrix, as was previously discussed. Further,according to one particular embodiment of the present invention (thatwill be discussed with reference to FIGS. 7A, 7B, and 8) the spectralcharacterization is first modified in the frequency domain and then,depending upon whether such modification is required to meet filterdesign criteria, additional spectral modifications are made in the timedomain.

FIG. 7A is a simplified block diagram illustrating an embodiment of anarrow band interference cancellation filter used in accordance with theprinciples of the present invention. In FIG. 7A, a narrow bandinterference cancellation filter (ICF) 10 is operative to null outnarrowband interference such as ingress and/or adjacent channelinterference from an input signal. In the embodiment, ICF filter 10generates a set of adaptive, predictive filter tap weights, based on aweight computation algorithm. In the embodiment, the ICF tap weights arecomputed during idle slots when interference is present, but no desiredsignal is present.

ICF filter 10 includes a delay element 12 to provide a unit delay of onesample, an adjustable finite impulse response (FIR) filter 14, and a tapweight computation circuit 16. An input signal having narrow bandinterference such as ingress or adjacent channel interference isreceived by ICF filter 10. The input signal is introduced to delayelement 12, tap weight computation circuit 16, and a summing junction18. Circuit 16 computes tap weights, which adjust the pass bandcharacteristics of FIR filter 14 to null out, i.e., cancel, narrow bandinterference appearing in the input signal. For example, if a noisespike or an intermittent carrier from another channel appears in thesignal, FIR filter 14 would be adjusted to have a notch at the frequencyof the interference appearing in the input signal. The output of delayelement 12 is coupled to FIR filter 14. FIR filter 14 filters the inputsignal based on the tap weights. In a typical embodiment of theinvention, FIR filter 14 might have 16 taps. The output of FIR filter 14is combined with the input signal in summing junction 18. The output ofsumming junction 18 is thus equal to the input signal with theinterference attenuated (or nulled).

The signal distortion caused by the ICF 10 may be compensated by adecision feedback equalizer (DFE) 15 in combination with a summing node19. The filter taps generated by the compute coefficients block 16 arealso provided to the DFE 15. The DFE 15 receives the filter taps and,based upon the filter taps, generates a compensation output that issummed with the input signal with ingress attenuated at summing node 19.The output of the summing node 19 is then received by a decision block17, which also receives input from the DFE 15. The decision block 17,based upon the input from the summing node 19 and the DFE 15, producesreceived data.

In FIG. 7B, the functional elements of the algorithm represented as tapweight computation circuit 16 of FIG. 7A are shown in more detail. Theinput signal from FIG. 7A, which includes narrow band interference suchas ingress and adjacent channel disturbances, is processed as shown inFIG. 7B. First, the narrow band interference is separated from thesignal. In the embodiment, a block of 256 complex samples is taken fromthe communication channel during idle Time Division Multiple Access(TDMA) slots, thereby representing the narrowband interference. Forexample, as illustrated in co-pending application Ser. No. 09/710,238,filed on Nov. 9, 2000, the MAC at a cable modem termination system couldcreate idle slots that insure there are time intervals in which no cablemodems are transmitting an upstream signal. The samples are taken duringthese idle slots. In another embodiment, the interference is separatedfrom the signal by estimating the signal (or a known portion thereof,such as a preamble) and subtracting the estimated signal from the inputsignal, leaving only the narrow band interference. In yet anotherembodiment, periods of no signal on the communication channel are sensedby a threshold detector or SNR detector, and the samples are only takenduring such periods. In any case, time intervals containing narrow bandinterference without signal are produced for sampling and processing bycircuit 16.

Preferably, the input samples are converted to the frequency domainusing a fast Fourier transform (FFT) block 22, which takes a complexfast Fourier transform of the input samples. Working in the frequencydomain facilitates a number of the operations described below such asverifying the narrow band content, tracking the changes in theinterference with different time constants, and frequency masking.However, the tap values could be computed in the time domain usingautocorrelation properties in another embodiment (as described in FIG.4). In the embodiment of FIG. 7B, a 256-point windowed FFT of the inputsamples is taken to produce a representation of the sampled spectrumwithout signal, i.e. the spectrum of the narrow band noise only.Typically, as many as 100 such blocks of samples are captured during atime interval T (typically, T=1 second), so as to permit rapid trackingof time varying narrow band interference.

The sampled spectrum without signal comprises 256 frequency bins eachhaving complex values that represent the energy in the frequency band ofthe bin. These frequency bins were illustrated generally with referenceto FIGS. 5 and 6. Preferably, the complex values of each bin areconverted to the absolute value of the result that is transformed to thelogarithm of the absolute value, thus providing an estimate of the logpower spectral density (PSD) of the interference.

To verify the narrow band content of the sampled energy, the output ofblock 22 is summed over all the frequency bins comprising the logspectrum, and this sum is compared to a threshold value, as representedby a block 30. If the sum exceeds the threshold value, then thatspectrum is discarded, and no further processing is performed duringthat particular time slot. The spectrum is discarded if the threshold isexceeded because that indicates that a large amount of the energy of thespectrum comprises strong wide band energy, or other content that isinconsistent with narrowband interference. If such a spectrum wereincluded in the computations made to generate the tap weights, insteadof being discarded, it could result in corrupting the narrow bandinterference spectrum estimate, and therefore the tap weights.

If the sum passes the threshold test, the spectrum is then introduced toa maximum-hold tracking filter represented by a block 32, which tracksthe changes in the interference with different time constants. Thistracking filter maintains an upper bound (with leak) on each frequencybin, over multiple input spectra, i.e., multiple blocks of inputsamples. Preferably, the tracking filter implements a fast-attack,slow-decay function, or a leaky maximum-hold function, depending on theparameters of the error gain function f(x) applied by filter 32. Asillustrated in FIG. 10, the error gain function f(x) is preferably anonlinear function with a high gain for large positive errors, therebycausing it to rapidly increase when a new narrow band interferencesource appears, and a very low gain for large negative errors, so thatthe spectrum estimate remembers an interfering signal for a period oftime after the narrow band interference disappears. Therefore, in thecase of intermittent narrow band interference at a given frequency, thefilter does not require significant retraining every time the narrowband interference reappears. In summary, the tracking filter produces anaverage of the input spectra taken over different periods of time,depending upon the error gain function.

Preferably, different gain characteristics similar to those shown inFIG. 10 are applied to the frequency bins individually or in groups. Forexample, eight different characteristics could be used and three controlbits could be assigned to each bin in order to select the gaincharacteristic for that bin. Depending upon the particular interferencepattern of the communication channel, the control bits are selected tocombat the interference. The output of the tracking filter block 32 isthen applied to a frequency mask represented by a block 44, where auser-defined spectrum mask may be applied to cancel selected frequencybins of the filtered spectrum leaving the tracking filter.

Each of the frequency bins of the mask has associated with it two modebits, designating how the mask and the spectrum are to be combined. (SeeFIG. 9.) The user-defined mask may indicate the presence of overlapping,adjacent-channel energy, or a known, narrowband interferer, or the like.The spectrum of the permanent energy spectrum of the channel can bemonitored either by a human observer or automatically by spectrumanalysis and intelligent computer algorithms, to identify for exampleadjacent overlapping channel energy or known interference sources; themask cancels these unwanted energy components in a flexible way based onthe selected mode. The user may select from a number of different modesof combining the inputs to block 44 on a bin by bin basis. For example,the input spectrum could be used en toto and the mask disregarded; themask could be used en toto and the input spectrum disregarded; thelarger of the input spectrum or the mask could be used or the sum of theinput spectrum and the mask in dB could be used. Each combining methodis selected independently from bin to bin.

The noise floor of the spectrum is also adjusted according to thepresent invention in mask block 44. Such noise floor alteration wasdescribed with reference to FIGS. 5 and 6 and, while performed by block44 of FIG. 7B, may be performed within or adjacent to tracker block 32.The concept of an SNR goal is used to determine how high the noise floorshould be in a robust design. For example, for a communications systemthat uses uncoded 16-QAM modulation, a goal of 20 dB is required forgood bit error rate (BER) performance. If the thermal noise floor isvery low, for example if 30 dB energy per symbol to noise density ratio(Es/No) is available, then it is not necessary to null out the narrowband interference all the way down to the white noise floor. Forexample, a 0 dBc interferer only needs to be nulled by a little over 20dB, not by 30 dB. In this example, one would raise the noise floor byapproximately 10 dB.

The noise floor of the spectrum is raised to the SNR goal for thefollowing reasons:

-   -   It tends to make the numerical computations of the filter        coefficients more robust.    -   It helps whiten the noise floor, de-emphasizing small peaks that        may have been caused by the tracking filter.    -   It tends to reduce the size of the ICF tap weights, which is        desirable from the standpoints of demodulator tracking and DFE        error propagation.

In adjusting the noise floor via mask block 44 operations, the spectrumis processed to find those frequency bins that correspond tointerference peaks, and those that comprise a noise floor. One way toperform this processing is by simple comparison with a threshold.Another way (preferred) is by sorting the spectrum into peaks and flooron a bin by bin basis. The floor bins are averaged in dB and adjusted toproduce the desired SNR goal. The spectrum intensity in these noisefloor bins (those below the threshold) is then further adjusted to thepoint where the budgeted SNR goal of the communication system is justmet. It is assumed in this discussion that the spectrum being processedcontains higher SNR than the SNR goal; if not, insufficient marginexists and the noise floor may not be beneficially raised.

After application of the mask and raising of the noise floor, thespectrum is converted to its anti-log and the inverse FFT function isperformed on the output of block 44 to return to the time domain asrepresented by an autocorrelation block 46. If n is the number of tapsin FIR filter 14, the first n time bins of the IFFT at the output ofblock 46 are used to produce the coefficients of the taps of filter 14.After computation of the tap weights and comparison with constraints, itis determined if further modifications to the noise floor should bemade; this is addressed in FIGS. 8, 11, and 12. If further changes inthe noise floor are needed, then, as represented by R(0) adjust block48, these changes may be accomplished via adjustment of the R(0) term ofthe autocorrelation function, which is output by block 46.

The noise floor from block 48 and the first n−1 time bins of the IFFTare supplied to a prediction algorithm for computing predictioncoefficients as represented by a block 52. The prediction coefficientsare based on past spectrum values. The prediction algorithm could be astandard algorithm for predicting a stationary time series from thefinite past (such as the Trench algorithm discussed herein below). Thefilter coefficients computed by the prediction algorithm of block 52 aretested against three tap constraints represented by a block 50. If theconstraints are not satisfied, an adjustment is made to R(0) by block 48until the prediction coefficients do fall within the constraints. It mayalso be necessary to recompute the prediction coefficients with aconstraint imposed. The prediction coefficients are inverted in polarityand used as the ICF tap weights for FIR filter 14 (FIG. 1B).

The R(0) adjust of block 48 can be used to establish an artificial noisefloor for the algorithm. In such case, the R(0) adjust in essencesimulates noise that either augments or reduces the actual noise on thecommunication channel and the algorithm responds accordingly tocalculate ICF tap coefficients that have a built in positive or negativemargin.

An algorithm shown in FIG. 11 carries out the constraints represented byblock 52. The autocorrelation function produced by autocorrelation block46 is processed by a step represented by a block 60 using the predictivealgorithm of block 50 to compute an initial set of ICF tap weights to betested by the constraints. The ICF tap weights resulting from block 60are compared with a constraint A at query block 62. In one embodiment,constraint A is that the lowest order tap magnitude be smaller than0.25.

If constraint A is satisfied, i.e., passes, then the operation proceedsto a query block 64, where the process determines whether constraints Band C are satisfied. In one embodiment, constraint B is that the sum ofthe tap magnitudes is smaller than 3, and constraint C is that theabsolute values of the real and imaginary parts of all prediction taps(I and Q) is less than 2.

If both constraints B and C are also satisfied, then the ICF taps areoutput and the algorithm is finished. If one or both are not satisfied,i.e., fail, however, then operation proceeds to a step 65, and the R(0)value of the autocorrelation function is adjusted. This adjustmentchanges the input autocorrelation function. Operation then proceeds backto the step represented by block 60 to recompute the predictioncoefficients and repeat the described steps.

However, if at query block 62 constraint A is not satisfied, then theoperation proceeds to a step 66 and a constraint flag is then set, whichfrom then on results in the invocation of a constrained algorithm, whichincorporates the magnitude of the first tap as a constraint on thesolution. Operation then proceeds to a query block 68, where the processdetermines whether constraints B and C are satisfied. If so, then theoperation proceeds back to the step represented by block 60 torecalculate the prediction coefficients using the constraint.

If at query block 68 one or both of constraints B and C are notsatisfied, then operation proceeds to a step 65, the R(0) value isadjusted, and operation then proceeds back to step 60.

In one embodiment of the invention, an iteration limit is enforced onthe number of times the prediction coefficients are computed pursuant toblock 60. For example, the prediction coefficients may be computed up tofive times in an effort to satisfy the three constraints. If after theselected number of iterations, the constraints are still not satisfied,then the previous ICF tap weights are allowed to persist.

In a further improvement, rather than beginning the design iterationcycle of FIG. 11 afresh with each updated spectrum estimate, a means ofincorporating information from previous designs is employed to shortentime required to calculate the prediction coefficients. This is shown inFIG. 12, in which the results of the initial tap weight computation,together with the results of the initial computation and lastcomputation of the previous design, are used to determine the parametersfor the second computation of the current design. The previous designrefers to the final ICF taps output from block 64 after the lastcalculation and the current design refers to the ICF taps being derivedin the course of the present calculation. In FIG. 12, the elements usedin the algorithm of FIG. 11 are assigned the same reference numerals. Inone embodiment, a state memory 70 stores the following state variablesfrom the previous design (filter setting generation operation):

-   -   Whether constraint A failed on 1^(st) iteration of previous        design.    -   Whether constraint A failed on any iteration of previous design.    -   First R(0) adjustment of previous design.    -   Cumulative R(0) adjustment predicted by previous design for use        in current design.

These state variables, or other selected suitable variables used in thealgorithm, are taken into account in computing the R(0) adjustment ofthe current design, as represented by a double ended arrow 72 betweenblocks 70 and 65. In addition, a leakage factor is included in thememory characteristics of state memory 70, as represented by an arrow74. The leakage factor determines the weighting given to the past valuesof the variable relative to the present values of the variables. If theleakage factor is zero, the past has no influence, i.e., it is ignored.If the leakage factor is unity, the influence from the past is fullyweighted. Normally a value slightly less than unity is used for theleakage factor. As represented by a broken line arrow 76 and arrow 72 tostate memory 70, the state variables are transmitted to state memory 70.

In a further improvement of the algorithm of FIG. 11, as represented inFIG. 12, by a query block 78, the iterations continue as time permits,further refining the noise floor/R(0) value. The R(0) value may befurther increased or reduced. For example, there may be time for threeiterations of the algorithm of FIG. 11 before the arrival of a newspectrum estimate. The algorithm may initially adjust the noise floor onthe first iteration by a large amount for maximum convergence speed. Thesecond iteration will then meet the constraints with excess margin. Thiswill lead to a lowering of the noise floor on the third iteration, whichas a result may or may not satisfy the constraints. The next design canthen benefit from the previous design. In this fashion, a designsatisfying a new interference spectrum can be found and applied quickly.The most recent design that passed the constraints is the one to use inthe ICF. Iterations that are more recent, but did not pass theconstraints, or did not complete due to lack of time, are not used.However, the most recent iteration, regardless of whether it passed theconstraints, is stored in the state memory for use by the next design.

First, an initial unconstrained computation is performed in the mannerdescribed in FIG. 11. The results of this initial tap weightcomputation, together with the results of the initial computation andlast computation of the previous design, are used to determine theparameters for the second computation of the current design. Forexample, in one embodiment there may be time for three iterations ofFIG. 11 before the arrival of a new spectrum estimate. The algorithm mayinitially adjust the noise floor on the first iteration by a largeamount for maximum convergence speed. The second iteration will thenmeet the constraints with excess margin. This will lead to a lowering ofthe noise floor on the third iteration, which as a result may or may notsatisfy the constraints. The next design can then benefit from theprevious design. In this fashion, a design satisfying a new interferencespectrum can be found and applied quickly.

The unconstrained algorithm employed to compute the filter settings isdescribed in a paper: William F. Trench, “Weighting Coefficients for thePrediction of Stationary Time Series From the Finite Past,” SIAM Journalof Applied Math, Vol. 15, No. 6, November 1967, pp 1502-1510(Hereinafter “Trench”, “Trench paper” and/or “Trench operations”).

In using the Trench operations, it is necessary to begin with theautocorrelation function of the process which is to be predicted (i.e.,predicted and then canceled in our application) when designingprediction taps using the Trench operation equations. We develop anestimate of the desired autocorrelation function in our ICF processingby inverse Fourier transforming the power spectrum of the ingress(tracked, masked, and otherwise processed to be made most suitable fornoise canceling). When N taps are to be used in the prediction (orcanceling) filter, plus the initial or feed-through tap, then theautocorrelation function is needed for displacements of zero to N, i.e.,R(n), n=0 to N. In the traditional case of designing prediction tapsusing the equations from Trench, the recursion formulae presented inTrench's Theorem 1 suffice, and the N+1 values of the autocorrelationfunction are all that is necessary as input to the recursions.

In the case of the constrained first tap magnitude operations, however,the traditional design procedure is not sufficient. If one design of theprediction taps is completed in the traditional way, and it is foundthat the first tap magnitude is larger than desired (constraint A), forwhatever reason, then a remedy for this situation is as follows. Assumethat the first tap in the prediction filter just derived is given as p₁,and that it is desired to limit its magnitude to p1_limit. We thendesire to find the best values for the remaining prediction taps, p₂ top_(N), given that the first tap now must be constrained.

We call upon Theorem 2 in Trench to solve this more difficult problemthan the original, unconstrained design. As in the traditionalunconstrained design, we use our autocorrelation function, R(n), n=0 toN, as the input to the recursions of the reference. Our autocorrelationfunction R(n) is used to define the autocorrelation matrix Φ_(r−s), forr,s=0 to N−1, of Reference 1, in the traditional unconstrained design.

To accommodate the constrained design, we make the followingmodifications:

1. In Equation 9 of the Trench paper, we now define the matrix Φ_(r-s),for r,s=0 to N−2, using R(n), n=0 to N−2. Further, in Equation 9 ofReference 1, we define η_(r), for r=0 to N−2, asη_(k) =R(k+2)−[R(k+1)p ₁ /|p ₁ |][p1_limit], k=0 to N−2.

2. Then, the recursions of the Trench paper, Theorem 2, are carried out.

3. After completing the recursions of the Trench paper, the solution toEquation 9 is developed, and is denoted in the reference as ξ_(0m),ξ_(1m), . . . ξ_(mm), where we have m=N−2 in our constrained case here.

4. The prediction tap coefficients for this constrained tap design caseare thenp _(1,constrained) =[p ₁ /|p ₁ |][p1_limit];p _(n,constrained)=ξ_(n−2,N−2), for n=2 to N.

5. The N values p_(n,constrained), n=1 to N, constitute the Ncoefficients for the desired prediction filter. Note that since noisecanceling is our desire, the additive inverse of these coefficients isused in the ingress canceling filter. These operations conclude oneembodiment of the constrained algorithm.

FIG. 8 is a logic diagram illustrating operations performedcorresponding to the filter weight computation algorithm of FIG. 7B in adifferent manner. The operations of FIG. 8 are performed for eachsampling interval. Operation commences by sampling the communicationchannel to produce the sampled signal (step 802). If the communicationchannel is sampled while a signal of interest (desired signal) ispresent, the signal of interest is removed from the sampled signal (step804). The sampled signal is then converted to the frequency domain toproduce a spectral characterization of the sampled signal (step 806). Anexample of such a spectral characterization is illustrated in FIG. 5.

The spectral characterization of the sampled signal is then compared toa threshold to determine whether the sampled signal is valid (step 808).One particular technique for performing this determination was describedwith reference to block 30 of FIG. 7B. If the spectral characterizationis discarded, the operation of FIG. 8 for the particular sampled signalis complete and operation returns to step 802 wherein the communicationchannel is sampled during the next sampling interval.

If the spectral characterization is valid, one or more trackingfunctions are applied to each spectral component of the spectralcharacterization (step 810). Then, the user mask is applied to eachspectral component (step 812). These operations were described in detailwith reference to FIG. 7B. Next, the noise floor of the spectralcharacterization is raised to a budgeted SNR (step 814). This operationwas described with particular reference to FIGS. 5, 6, and 7B. All ofthese operations of step 810, 812, and 814 are performed in thefrequency domain. The result of these operations is the production of amodified spectral characterization. The modified spectralcharacterization is then converted to the time domain (step 816).

With the modified spectral characterization in the frequency domain, afirst set of filter coefficients for the sample interval is computed(step 818). This operation may be performed according to the Trenchpaper. With the filter coefficients determined, a determination is madeas to whether constraint A is passed (step 820). In the describedembodiment, constraint A is passed if the first tap coefficientmagnitude (after monic tap) is less than 0.25. If constraint A ispassed, the filter coefficients are compared to constraints B and C. Inthe described embodiment, constraint B considers whether the sum of thetap magnitudes is less than 3.0 and constraint C considers whether themax tap I or Q component is less than 2.0. If all of these constraintsare met at steps 820 and 822, the filter taps are applied to FIR filter14 of FIG. 7A.

If, however, constraint A is not met, all subsequent filter coefficientdetermination is performed using the constrained algorithm describedabove herein (block 824) and operation proceeds to decision block 826.If constraint A is met but constraints B and/or C are not met, operationproceeds to decision block 826 but the unconstrained algorithm continuesto be employed. If a time out condition has occurred at decision block826, e.g., four iterations, actual time constraint, etc., operation endswithout determination of new filter coefficients. In such case, aprevious set of filter coefficients are employed.

If the time out condition of decision block 826 has not occurred, themodified spectral characterization and/or filter tap settings producedmay be modified using prior operations/prior solutions (block 828).Then, the noise floor is adjusted (block 830) and either the unmodifiedor the modified algorithm is employed to compute again the filtercoefficients (block 818). The operations of FIG. 8 will continue to beperformed for the current sample until either a solution is produced ora time out condition is met.

FIG. 13 is a system diagram illustrating the manner in which the presentinvention may be employed in a cable telecommunication system servicinghigh speed data communications. In the cable telecommunication system, ahead end unit 1302 couples to a plurality of cable modems 1304A-1304Evia respective cable media 1306A-1306E. The head end unit 1302 serviceshigh speed data communications for the plurality of cable modems1304A-1306E by acting as a gateway to a data network, such as theInternet 1308, a telephone network 1312, or other services network 1314.The head end unit 1302 also acts to distribute cable TV signals via thecable media 1306A-1306E to TV set top boxes (not shown). These cable TVsignals therefore share the cable media 1306A-1306E with the high speeddata communications also serviced by the head end unit 1302.

The head end unit 1302 services the high speed data communicationswithin a frequency band of interest. Interfering signals may residewithin this frequency band of interest. Once source of these interferingsignals may be radio frequency (RF) coupled from an RF source 1308. HAMradio operators, for example, produce radio frequency emissions in thefrequency band of interest that may couple to the cable media1306A-1306E as interfering signals. RF coupled interfering signals aretypically intermittent. However, some interfering RF signals may bepredicted. Further, the cable system itself may produce interferingsignals via adjacent channel interference or via infrastructurecomponents that operate in a faulty manner. These signals may beintermittent or may be somewhat continual, depending upon the cause ofthe interfering signals.

Thus, according to the present invention, the head end unit 1302 and/orthe cable modems 1304A-1304E employ the filtering operations of thepresent invention to remove the interfering signals from thecommunication channel serviced by the cable media 1306A-1306E. Byperforming these operations, the communication channel will service datacommunications at a higher effective data rate, thus increasing thethroughput supported by the cable telecommunication system.

In general, the described algorithms could be implemented in softwarethat operates on a special purpose or general-purpose computer or inhardware. If the calculations necessary to compute the filter tapcoefficients have not been completed by the next time interval T, thetime interval is simply skipped and the algorithms operate on the narrowband interference energy in the following time interval T. Withreference to FIG. 87 of application Ser. No. No. 09/710,238, filed onNov. 9, 2000, the invention disclosed in this application comprisesnotch filter adjusting block 377.

The described embodiment of the invention is only considered to bepreferred and illustrative of the inventive concept; the scope of theinvention is not to be restricted to such embodiment. Various andnumerous other arrangements may be devised by one skilled in the artwithout departing from the spirit and scope of this invention. Thevarious features of the invention such as verifying the narrow bandcontent, tracking the changes in the narrow band interference withdifferent time constants, and frequency masking could each be practicedseparately. For example, after integrating the frequency domainrepresentation of the communication channel upon which the ICF isoperating, the ICF coefficients could be computed in the time domain,instead of the frequency domain. Alternatively, the tracking functioncould be performed on the time domain representation of thecommunication channel. Furthermore, the features of invention can bepracticed in other types of communication channels such as fixedwireless, cable, twisted pair, optical fiber, satellite, etc.

1-51. (cancelled)
 52. A method for canceling narrow band interference ona communication channel, the method comprising the steps of: samplingthe communication channel to produce a sampled signal; spectrallycharacterizing the sampled signal across a frequency band of interest toproduce a spectral characterization of the sampled signal, wherein thespectral characterization does not include a signal of interest;modifying the spectral characterization of the sampled signal to producea modified spectral characterization; generating filter settings basedupon the modified spectral characterization; and filtering thecommunication channel using the filter settings when the signal ofinterest is present on the communication channel.
 53. The method ofclaim 52, wherein in modifying the spectral characterization of thesampled signal to produce the modified spectral characterization, aplurality of spectral characteristics are modified.
 54. The method ofclaim 52, wherein in modifying the spectral characterization of thesampled signal to produce the modified spectral characterization, anoise floor of the spectral characterization is raised to meet abudgeted signal-to-noise level.
 55. The method of claim 52, wherein inmodifying the spectral characterization of the sampled signal to producethe modified spectral characterization, spectral componentscorresponding to an expected interfering signal are modified.
 56. Themethod of claim 52, wherein in modifying the spectral characterizationof the sampled signal to produce the modified spectral characterization,spectral components corresponding to a plurality of expected interferingsignals are modified.
 57. The method of claim 52, wherein in modifyingthe spectral characterization of the sampled signal to produce themodified spectral characterization, spectral components of prior sampledsignals are employed.
 58. The method of claim 57, wherein in consideringthe spectral components of prior sampled signals in modifying thespectral characterization of the sampled signal to produce the modifiedspectral characterization, the presence of an interfering signal in aprior sampled signal is weighted more heavily than the absence of theinterfering signal in the current sampled signal.
 59. The method ofclaim 52, wherein the frequency band of interest corresponds to thefrequency band of the signal of interest.
 60. The method of claim 52,wherein the sampling is performed when the signal of interest is notpresent on the communication channel.
 61. The method of claim 52,wherein: the sampling is performed while the signal of interest ispresent on the communication channel; and the method further comprisesremoving the signal of interest from the sampled signal.
 62. The methodof claim 52, wherein modifying the spectral characterization of thesampled signal to produce a modified spectral characterization isperformed in the frequency domain.
 63. The method of claim 52, whereinmodifying the spectral characterization of the sampled signal to producea modified spectral characterization is performed in the time domain.64. The method of claim 52, wherein modifying the spectralcharacterization of the sampled signal to produce a modified spectralcharacterization is performed both in the frequency domain and in thetime domain.
 65. The method of claim 52, further comprising discardingthe sampled signal when total spectral density of the spectralcharacterization of the sampled signal exceeds a threshold.